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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3282 条记录，以下是811-820 订阅

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Human Action recognition Based on Spatio-Temporal Feature Extraction from Skeleton data

Human Action Recognition Based on Spatio-Temporal Feature Ex...

引用

Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Haiming Sun Song Wang Zhenming Zhang Juhuai Yan Lin Ma Weiyao Xu Weihai Comprehensive Research Center of Electronic Information Technology in the MIIT Weihai Shandong China Weihai Ocean Vocational College Weihai Shandong China Rongcheng Municipal Bureau of Industry and Information Technology Rongcheng Shandong China Zaozhuang University Zaozhuang Shandong China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Compared with RGB data, human action recognition based on skeleton data does not contribute to the influences of background occlusion, changes of light, and viewpoints. Moreover, the quantity of data needed for the human skeleton data is small, they can increase the recognition speed and efficiency. This paper raises the problem left by conventional learning methods, that is, how to extract multi-scale temporal and spatial features from data samples insufficiently Due to this, the spatio-temporal feature extraction algorithm introduced in this paper that can extract both the spatial attitude features and the multi-scale temporal displacement features at the same time. This proposed algorithm is tested on two public sets and contrasted with other existing algorithms. These experiments reveal that the recognition performance of the proposed algorithm is better than the conventional algorithms.

关键词： learning systems Software algorithms Feature extraction Skeleton Spatial databases Encoding Classification algorithms data mining Human activity recognition Software engineering

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Predictive Models for Career Guidance, Based on Academic and Behavioral data with data mining and machine learning Technique

Predictive Models for Career Guidance, Based on Academic and...

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Disruptive Technologies (ICDT), international conference on

作者： Brijesh Kumar Verma Nidhi Srivastava Ajay Kumar Bharti Amity Institute of Information Technology (AIIT) Amity University Lucknow U.P INDIA Ambalika Institute of Management and Technology (AIMT) Lucknow U.P INDIA

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

A person's future is greatly influenced by career counseling, especially for students making decisions about their education and careers. This project focuses on using data mining and machine learning approaches to leverage behavioral and academic data to create prediction models for career advising. To produce individualized career suggestions, the suggested system combines data gathering, preprocessing, feature engineering, and predictive analytics. Academic performance indicators (e.g., grades, test scores), behavioral characteristics (e.g., learning styles, personality tests), and demographic information are important sources of data. While clustering techniques like K-Means make it easier to group students with similar profiles, machine learning algorithms like Random Forest, Support Vector machines, and Neural Networks are used for classification. Furthermore, hybrid models that combine clustering and classification improve the accuracy and customization of the system. Metrics including precision, recall, F1 score, and clustering validation indices are used to evaluate the system, guaranteeing solid and trustworthy results. By assigning students to appropriate career groupings and recommending certain positions, the prediction algorithms offer useful insights. This study fills in the gaps in conventional career counseling approaches and opens the door for scalable, effective, and customized career advice systems by showing how cutting-edge data-driven approaches can enable students to make well-informed career decisions.

关键词： Support vector machines Employee welfare Accuracy Engineering profession Stability criteria Neural networks Predictive models data models data mining Random forests

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Facial Expression recognition using machine learning models in FER2013 3

Facial Expression Recognition using Machine Learning models ...

引用

3rd IEEE international conference on Frontiers Technology of Information and Computer, ICFTIC 2021

作者： Luo, Jinyu Xie, Zhuocheng Zhu, Feiyao Zhu, Xiaohu University of Toronto Department of Statistics TorontoON Canada Mathematics and Shanghai University Shanghai China Hefei University of Technology School of Mathematics of HFUT Hefei China Arizona State University Ira A. Fulton Schools of Engineering Tempe United States

ISBN: (纸本)9781665406055

Facial expression recognition aims to classify human expression using the designed algorithm. Recently, the popular FER2013 dataset suffered several bottlenecks for learning a good model. In this paper, we aim to design a data augmentation method to solve the issues in FER2013. Furthermore, we compare four different machine learning methods, including SVM, AlexNet, VGG, and ResNet. The experiments on FER2013 show VGG16 model is the best model for facial expression recognition, and our data augmentation achieves a significant performance promotion in FER2013. Our method provides an in-depth research on image classification, which can be widely applied in most of real-world applications. © 2021 IEEE.

关键词： Face recognition

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Self-supervised Image Classification Using Convolutional Neural Network

Self-supervised Image Classification Using Convolutional Neu...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Shiyun Kong Shanghai United International School Shanghai China

Image classification has been a trendy research topic in the field of pattern recognition and computer vision, which extracts different features of images and predict the category of images. Thanks to the development of deep learning, powerful convolutional neural networks can be used in the field of image recognition. However, existing deep learning-based image recognition research mostly follows the framework of supervised learning, and model learning relies on a large number of accurate labels. Providing a large amount of label data will undoubtedly require laborious human effort and expensive costs. Therefore, image recognition based on unsupervised learning (without using any image category labels to achieve classification) has become a spotlight for research. In this paper, explorations on the image classification by self-supervised framework SimCLR on image classification successfully clusters a large number of images into an optimum amount categories. Qualitative results have show SimCLR is particularly effective in recognizing the colors of images; both qualitative and quantitative results shows SimCLR is great at identifying simple contours. However, when the colors are similar, and contour lines are complex, SimCLR does not obtain satisficing results. The accuracy on classifying Mnist dataset is 32%.

关键词： Image recognition Image color analysis Supervised learning Feature extraction Power electronics pattern recognition Convolutional neural networks

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Design of a Real-Time Crime Monitoring System using machine learning

Design of a Real-Time Crime Monitoring System using Machine ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： A. Krishnaveni C. Manikandan Department of Computer Science Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Crime in public areas poses risks to individuals and society at large. Traditional crime prevention methods face significant hurdles in detecting and tracking crime. Furthermore, the influx of large volumes of data overwhelms security systems and personnel making security enforcement harder. However, technological innovations have positively influenced different fields, including public safety and crime prevention. This study introduces an innovative system for crime prediction and analysis using machine learning, designed to assist law enforcement in identifying criminals in public spaces. The system enables authorities to input detailed information about criminals, including facial data, which is used to train an advanced recognition model. The system's ability to provide accurate and timely alerts enhances the efficiency of law enforcement operations. This framework leverages state-of-the-art face recognition technology to offer a scalable and robust solution for crime prevention. By integrating machine learning algorithms with intelligent surveillance systems, the proposed approach addresses the growing need for proactive measures to ensure public safety. Further, this system also highlights the potential of technology to transform traditional crime detection methods, paving the way for smarter and more responsive law enforcement practices. Finally, the proposed system attained a 92% face detection accuracy score which was higher than existing approaches.

关键词： Accuracy Recurrent neural networks Law enforcement Face recognition Surveillance Prevention and mitigation Speech recognition Real-time systems Public security data models

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Design and Application of English Pronunciation Correction Algorithm Based on SVM+LDA

Design and Application of English Pronunciation Correction A...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Huizhong Yu Ting Zhou Jiangsu College of Tourism Yangzhou Jiangsu China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

For the common pronunciation errors of English learners in the pronunciation process, an English pronunciation error correction algorithm based on SVM (Support Vector machine) and LDA (Latent Dirichlet Allocation) is introduced. The algorithm aims to accurately identify and correct the problems in learners' pronunciation, thereby improving their pronunciation accuracy and fluency. By combining the feature extraction of speech signals with machine learning methods, different types of pronunciation errors can be effectively distinguished and corrected. First, the pronunciation data of English learners are collected and preprocessed to extract speech features, including MFCCs (Mel-Frequency Cepstral Coefficients), formants, pitch, etc. Secondly, LDA is used to reduce the dimension of the extracted features to reduce the complexity of the feature space and enhance the ability to distinguish pronunciation errors. Then, SVM is used for classification training to train a pronunciation error model. Finally, the trained model is used to predict the learner's pronunciation, and corresponding pronunciation correction suggestions are made according to the classification results. This method can effectively identify different types of pronunciation errors and provide personalized correction feedback according to the nature of the error. When the pronunciation error correction algorithm based on SVM+LDA is used for testing, the learners' error rate is 31.9% on average before error correction, and drops to 26.3% after error correction. The English pronunciation error correction algorithm based on SVM+LDA introduced in this study can effectively improve the pronunciation accuracy of English learners. This method has strong practicality and scalability, and can provide strong support for English pronunciation teaching and speech recognition technology.

关键词： Support vector machines Training Accuracy machine learning algorithms Systematics Speech recognition Feature extraction Error correction Classification algorithms data mining

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Person recognition using Gait Energy Image, MobileNetV3Small and machine learning

Person Recognition using Gait Energy Image, MobileNetV3Small...

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Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： Omprakash Suthar Vijay Katkar Krunal Vaghela Department of Computer Engineering Marwadi University Rajkot India Department of Information Technology Marwadi University Rajkot India

In the past few decades, gait as a biometric attribute has garnered a lot of attention for its application in a broad variety of security and privacy-related scenarios, including identity recognition and authentication. The deployment of surveillance cameras allows for the collection of Gait data, which is a non-invasive form of identification that is based on a biometric trait that can be seen from a distance and can be utilized for identifying reasons. It is difficult to create reliable automatic gait identification systems since Gait can be modified by several covariate parameters. Some of these covariate elements include garments, walking tempo, camera view angle. In this work, a method for recognizing people with a high degree of accuracy is demonstrated. This paper makes use of the Gait Energy Image, MobileNetV3Sma11 model, and machine learning to develop an approach to recognize person using Gait pattern. The paper includes experimental findings, including precision, recall, f1-score, and classification accuracy, all of which point to the effectiveness of the suggested method.

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Deep learning-Based recognition of Handwritten English Characters

Deep Learning-Based Recognition of Handwritten English Chara...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Karpagalakshmi, R.C. Hegde, Sushant Sharma, Rajesh R. Mahapatra, Sheila Leni, Ezil Sam Sungheetha, Akey School of Advanced Computing Alliance University Bangalore India School of Liberal Arts Alliance University Bangalore India

This paper presents a Convolutional Neural Network (CNN) model for handwritten character recognition. The architecture comprises three convolutional layers with ReLU activation functions with increased filter sizes like 32, 64 and 256 with max pooling, designed to extract relevant features. The model transitions to dense layers and culminates in a SoftMax output layer for classifying 26-character classes. To enhance performance and prevent overfitting, we implement dropout regularization, data augmentation techniques, early stopping, and learning rate reduction. The results demonstrate high accuracy and generalization, underscoring the efficacy of CNNs in image recognition tasks. This work contributes to the advancement of automatic handwritten character recognition, with potential applications in document digitization and educational technology. © 2025 The Author(s).

关键词： Optical character recognition

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E-Commerce Big data Analysis and User Behavior Prediction Algorithm Based on Deep learning

E-Commerce Big Data Analysis and User Behavior Prediction Al...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Qianwen Lv Fushun Vocational and Technical College Fushun Liaoning China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

With the vigorous growth of big data technology and intelligent interaction technology, e-commerce has become a widely concerned focus of the public. In the field of e-commerce, how to accurately predict shopping behavior and achieve personalized recommendations from massive user data has become a top priority of research. Big data analysis plays a crucial role in the field of e-commerce, as it can extract potential patterns from complex and ever-changing data, extract useful information, and provide decision support for businesses. Deep learning (DL), as an important branch of machine learning (ML), also shows great potential for application in the field of e-commerce. This article proposes a DL based e-commerce big data analysis and user behavior prediction algorithm. This algorithm accurately predicts the future shopping behavior of users by deeply mining potential features in user behavior data, combined with the powerful feature representation and learning ability of DL models. The experimental results show that the algorithm performs well in predicting user behavior, effectively improving the personalized recommendation effect and user experience of e-commerce platforms, and providing strong technical support for e-commerce enterprises.

关键词： Deep learning Accuracy machine learning algorithms Consumer behavior Big data Predictive models Prediction algorithms data models Electronic commerce data mining

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MANet: Multimodal Attention Network based Point-View fusion for 3D Shape recognition 25

MANet: Multimodal Attention Network based Point-View fusion ...

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25th international conference on pattern recognition (ICPR)

作者： Zhao, Yaxin Jiao, Jichao Li, Ning Deng, Zhongliang Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728188089

3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point-cloud data or multi-view data alone. However, in the era of big data, integrating data of two different modals to obtain a unified 3D shape descriptor is bound to improve the recognition accuracy. Therefore, this paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features, and then realize the effective fusion of the two features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition, and then fuse the point-cloud features and the enhanced multi-view features to obtain a more discriminative 3D shape descriptor. We have performed relevant experiments on the ModelNet40 dataset, and experimental results verify the effectiveness of our method.

关键词： point-cloud multi-view 3D shape recognition point-view fusion multimodel attention network

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